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This chapter describes two innovative cloud data security frameworks: Distributed Encryption Key Management (DEKM) and Insider Threat Detection and Attribution (ITDA). DEKM improves safe data sharing by spreading encrypted data to the cloud and fragmenting encryption keys among verified parties. A Key Orchestrator regulates access control and key reconstruction using an enhanced Lagrange interpolation approach. ITDA combats data leakage by using sophisticated detection and source attribution methods. Both frameworks were tested using CloudSim, with an emphasis on key generation time, response latency, and encryption/decryption efficiency. DEKM integrates secure VM deployment and is evaluated using time consumption and probability estimations. Experimental findings show that these techniques significantly decrease security concerns, increase data confidentiality, and improve overall system performance when compared to traditional methods. The frameworks provide potential solutions for tackling important data protection concerns in cloud contexts while balancing strong security and operational efficiency.

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Data Security and Leakage Detection Models

  • 章节

  • 发表日期：

    2025年

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  • Sakshi Patni,
  • Deepika Saxena,
  • Ashutosh Kumar Singh

摘要

This chapter describes two innovative cloud data security frameworks: Distributed Encryption Key Management (DEKM) and Insider Threat Detection and Attribution (ITDA). DEKM improves safe data sharing by spreading encrypted data to the cloud and fragmenting encryption keys among verified parties. A Key Orchestrator regulates access control and key reconstruction using an enhanced Lagrange interpolation approach. ITDA combats data leakage by using sophisticated detection and source attribution methods. Both frameworks were tested using CloudSim, with an emphasis on key generation time, response latency, and encryption/decryption efficiency. DEKM integrates secure VM deployment and is evaluated using time consumption and probability estimations. Experimental findings show that these techniques significantly decrease security concerns, increase data confidentiality, and improve overall system performance when compared to traditional methods. The frameworks provide potential solutions for tackling important data protection concerns in cloud contexts while balancing strong security and operational efficiency.